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Systems Biology

Code: 101950 ECTS Credits: 6
Degree Type Year Semester
2500890 Genetics OB 3 2


Joan Albiol Sala

Use of Languages

Principal working language:
catalan (cat)
Some groups entirely in English:
Some groups entirely in Catalan:
Some groups entirely in Spanish:

Other comments on languages

Most of the provided additional material is written in English. Catalan, spanish or english are used interchangeably


Fundamental pre-grade knowledge in Algebra, Differential Calculus, Chemistry and Biochemistry.

Good comprehension of English written scientific publications and textbooks

Basic computer user skills (Windows, Word, Excel,...).

Be enrolled or have passed the Systems Biology module included in Integrated Laboratory VI.

Objectives and Contextualisation

Systems biology is a rapidly evolving field which fosters a new approach to solve biological problems through a combination of experimental data and the use of computer models with both predictive and explanatory power. The systems biology approach is centered in the integrated study of the network components (genes, enzymes, metabolites,…) and their interactions, revealing key emerging properties and complex dynamic behavior.

Historically, although it might be argued that the concept is much older, the systems biology approach evolves as a response to the enormous data accumulation from genomics, proteomics, transcriptomics, metabolomics,... and also due to the exponential increase in computer power allowing to go further in the analysis, interpretation and deeper understanding of the 'omics' data. 

The first objective of the course will be to review the motives and origins of the discipline while offering a perspective of its relevance in the near future.

The second objective is to introduce the student to the tools and methods most commonly used. Thus the course will evolve from the mathematical description of the system, through the alternative methods of solution, towards the analysis of the resulting behavior. As a result the student will know and be able to use the most frequent basic tools used nowadays in the field.

The third objective will be to apply the acquired knowledge to model systems of the three most studied subsystems, namely metabolic, genetic and signal transduction networks. The emerging dynamics of those systems allows to see the main traits that arise in complex systems and understand the necessity of the ‘systems’ approach. An important part of this objective is performed as practical computer simulation sessions included in the Integrated Laboratory VI.

The fourth objective includes a firsthand appreciation of how this new approach is being applied in present day research. To this purpose the students will review real examples from scientific literature. Part of this objective will be fulfilled as a team work including the presentation of a reviewed paper to the rest of the students. This activity will favor a deeper understanding of the concepts learned, foster a wider view of its real impact as well as promoting the development of the student communication skills.

The subject is presented gradually, advancing from the basic concepts towards the description of more complex systems allowing for a thorough understanding of the necessity to study systems as integrated units.

The general objective is to allow the student to acquire the systems perspective of today’s biology.



  • Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values. 
  • Apply knowledge of theory to practice.
  • Apply scientific method to problem solving.
  • Be able to analyse and synthesise.
  • Design experiments and interpret the results.
  • Develop self-directed learning.
  • Know and interpret the metabolic and physiological bases of organisms.
  • Make changes to methods and processes in the area of knowledge in order to provide innovative responses to society's needs and demands. 
  • Perceive the strategic, industrial and economic importance of genetics and genomics to life sciences, health and society.
  • Reason critically.
  • Take account of social, economic and environmental impacts when operating within one's own area of knowledge. 
  • Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
  • Use and manage bibliographic information or computer or Internet resources in the field of study, in one's own languages and in English.

Learning Outcomes

  1. Act with ethical responsibility and respect for fundamental rights and duties, diversity and democratic values. 
  2. Apply knowledge of theory to practice.
  3. Apply scientific method to problem solving.
  4. Be able to analyse and synthesise.
  5. Defend the relevance of progress in the generation and interpretation of data on a genomic scale for our understanding and technological manipulation of organisms.
  6. Describe the analysis of metabolic control.
  7. Design experiments and interpret the results.
  8. Develop self-directed learning.
  9. Explain the basics of metabology/metabolics and their methods.
  10. Make changes to methods and processes in the area of knowledge in order to provide innovative responses to society's needs and demands. 
  11. Reason critically.
  12. Take account of social, economic and environmental impacts when operating within one's own area of knowledge. 
  13. Take sex- or gender-based inequalities into consideration when operating within one's own area of knowledge.
  14. Use and manage bibliographic information or computer or Internet resources in the field of study, in one's own languages and in English.


1.- Introduction and definitions

1.1 The ‘systems’ perspective

1.2 Key general concepts. Emergence and robustness.

2.- Systems description and study

2.1 Top-down vs bottom-up approximations

2.2 Timescales

2.2 Deterministic vs. stochastic approaches

2.3 Dynamics vs steady state

2.4 Review of fundamental mathematical concepts

2.5 Introduction to system dynamics

2.6 Parameter determination

2.7 Structure, kinetics and thermodynamics

3.  Networks and biological systems

3.1 Networks and genetic circuits

3.2 Metabolic networks in steady state

3.3 Metabolic control analysis

3.4 Signal transduction networks


Along the learning process, the teaching methodology will be fundamentally based on the student’s work and the professor will guide the student either in the process of acquisition and interpretation of the most relevant information as well as in the student’s personal work. The student will collect as much learning evidences as possible in the student portfolio as detailed in the evaluation paragraphs. In this context, and according to the learning objectives of the course, the type of learning activities will include theory classes, exercise and problem solving classes, practical computer exercises and tutor sessions.

Theory classes: Were the main basic conceptual topics and the most relevant information will be provided so that the student can develop its autonomous learning. Computer slides (ppt or pdf format) will be available to the student in the virtual campus.

Seminar and problem solving sessions: These sessions will be done in a reduced subgroup of students of the class. Exercises, previously provided, will be explained and/or solved so that they contribute to learn and clarify the knowledge provided along the course. In those sessions, the students will also explain their peers the solution proposed and the pathway and difficulties encountered while solving them, so that the experience is shared among all of them. The exercises will be delivered through the virtual platform before the exercises are solved in class.

Team work: The students will develop a short essay based on a scientific publication of the field and will present it to their peers in class. This activity provides the opportunity to personally contribute for example by doing some directly related genetic, metabolic or signal transduction computer simulations. This information can be included in the tests to assess the student’s learning progress.

Practical computer sessions: Part of the learning outcomes will be acquired through practical computer sessions. Those sessions are formallyincluded in the Molecular Systems Biology module within Integrated Laboratory VI and therefore evaluated separately. Nevertheless they are necessary to achieve the learning outcomes of this topic. Those practical sessions will be done using ad-hoc software. Those exercises will allow the student to become familiar with the models and type of data most common in the field and their use.  Those exercises will be done using existing free software.

Tutoring: It will be possible to perform a couple of tutor sessions, if it is requested by the students. The main objective will to solve doubts, review basic concepts or guide in the process of selecting additional sources of information.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Title Hours ECTS Learning Outcomes
Type: Directed      
Seminars and problem solving sessions 15 0.6
Theoretical lessons 30 1.2
Type: Supervised      
Support sessions to individual or team work 2 0.08
Type: Autonomous      
Solve exercises individually with or without a computer 35 1.4
Study, review references, , ... 45 1.8
Team work 19 0.76


Assesment of the student’s acquired skills, competences and learning outcomes, will be based on the collection of the following evidences:

- Exercises solved   (26%)
Along the course, the student will perform a series of exercises, either manually as well as using a computer, which will subsequently be solved in class by one of the students. Those exercises will be provided by each student along the course before being solved in class. Should the exercises not be provided along the course, the mark of this part will be penalized with a decrease of up to 20 % of the qualification of the exercises. At the end of the course one more evaluable exercises will be delivered, which can be done in class, accounting for 80% of the exercises mark.
- Team work (26%)
Students will have to perform a team work based on a scientific publication on Systems Biology. The team, composed of a small number of members, will review the selected paper and if relevant, will attempt to reproduce any computer simulations using the corresponding free software. The work will also be presented to the rest of the students by all the team members. One of the main objectives of this part will be to transmit the information and knowledge gained by the group to the rest of the students.  With the purpose to evaluate the level of success in transferring that information, the rest of the students will evaluate the received information using and evaluation rubric. Nevertheless the presented information can also be evaluated at the progress test or at the final examination.
- Progress tests (48%)
Several tests will be performed along the course to evaluate the student's progress in achieving the learning outcomes. Should the student not to perform such tests or reach a qualification below 3.5, the student will have to perform a final test coveringall the subjects of the course.
Final examination/Retake (only if continuous evaluation is failed) (100%): Optionally, if the student shows insufficient progress, does not reach the minimum average evaluation level in the progress tests, or if is repeating the semester course, exists the possibility of taking a Final Exam/Retake including all the topics of the course. To be eligible for the retake process, the student should have been previously evaluated in a set of activities equaling at least two thirds of the final score of the course or module. It must be taken into account that if the student takes this option any previous qualification, deriving from the continuous evaluation activities, will be discarded and not considered furthermore.
Other aspects:
- No Avaluable (not evaluated): Any student not performing at least two thirds (67%) of the evaluation activities described previously will be qualified as ‘No Avaluable’. Also any student not reaching the minimum average qualification in the progress tests and not taking the Final Exam/Retake, will be qualified as ‘No Avaluable’.
- Matrícula de honor (MH). Excellent with honor or special mention. Grant a qualification with honor is a personal decision of the professorship responsible of the course. UAB regulations specify that an MH can only be given to students with a final qualification equal or above 9.00 but the professorship can fix a higher minimum level if there are more candidates than the maximum number of honors available, or request complementary activities . Furthermore, this qualification can only be given up to a 5% of the total number of students enrolled in the course.
- Calendar:The dates of any evaluation activity andthose of delivery of the team work will be given during the first class of the course. Thosedatescould be changed as a result of schedule reorganization or an adaptation to any unknown event at this time. Those changes will always be communicated through the virtual platform (Moodle) as it is understood as the main communication channel between students and professors.
- Plagiarism: Irrespective of any other measure that may be adopted, and according to the actual academic regulations, any irregular action committed by a student resulting in a variation of the result of any evaluation activity, will receive a qualification of zero. Therefore, to copy or let any other one to copy, during a test, and exercise or a practical activity will result in its failure. Should this activity be necessary to pass the course, the complete course will not be passed.  Any activity qualified this way cannot be recovered. Therefore the complete course will be failed without the opportunity to pass it during the same semester.

Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Progres test 48% 4 0.16 3, 2, 6, 7, 4
Solving exercises 26% 0 0 3, 2, 6, 8, 7, 11
Team work 26% 0 0 1, 13, 12, 3, 2, 5, 6, 9, 10, 11, 4, 14


Primary references

Alon, U. An Introduction to Systems Biology. Design principles of biological circuits. Second edition. Chapman & Hall/CRC. 2019. (http://web.b.ebscohost.com.are.uab.cat/ehost/ebookviewer/ebook?sid=d09d1d54-1767-460d-98bd-226103c59744%40pdc-v-sessmgr03&vid=0&format=EB)

Klipp, E., R. Herwig, A. Kowald, C. Wierling, i H. Lehrach. Systems Biology in Practice. Concepts implementation and application. Weinheim: Wiley-VCH, 2005.

Klipp, E., W. Liebermeister, C. Wierling, A. Kowald; Systems Biology. A textbook 2nd. Weinheim: Wiley-VCH, 2016.

Klipp, E., W. Liebermeister, C. Wierling, A. Kowald, H. Lehrach, Herwig R. Systems Biology. A textbook. Weinheim: Wiley-VCH, 2009.

Nielsen, J.; Hohmann, S. Systems Biology.  Wiley-Blackwell. 2017  (https://onlinelibrary-wiley-com.are.uab.cat/doi/book/10.1002/9783527696130)

Voit E. A First Course in Systems Biology. 2nd edition. Garland Science. 2017

Complementary references

Helms, V. Principles of Computational Cell Biology. From protein complexes to cellular networks. Weinheim: Wiley-VCH, 2008.

Ingalls B.P. Mathematical Modeling in Systems Biology: An Introduction. MIT Press. 2013

Konopka, A.K. Systems Biology. Principles, methods and concepts. Boca raton: CRC Press, 2007.

Kriete, A., i R. Eils, . Computational Systems Biology. Burlington: Elsevier Academic Press, 2006. (https://www-sciencedirect-com.are.uab.cat/book/9780120887866/computational-systems-biology)

Kriete, A., i R. Eils, . Computational Systems Biology. 2nd Edition. Elsevier Academic Press, 2014.(https://www-sciencedirect-com.are.uab.cat/book/9780124059269/computational-systems-biology)

Nielsen J., Hohmann S., Lee S. Y.  Systems Biology (Advanced Biotechnology) 1st Edition.Wiley-Blackwell , 2017.

Palsson, B.O. Systems Biology. Properties of reconstructed networks. Cambridge: Cambridge University Press, 2006.

Palsson, B.O. Systems Biology. Simulation of dynamic network states. Cambridge: Cambridge University Press, 2011.

Stephanopoulos G.N. Aristidou A.A. Nielsen J. Metabolic Engineering. Principles and Methodologies. Academic Press. San Diego. USA,  1998

Szallasi, Z., V. Periwal,i J. Stelling, . System Modeling in Cellular Biology: From Concepts to Nuts and Bolts. The MIT Press, 2006.


- COPASI (http://copasi.org/)

- Microsoft Excel

- Matlab